Computer implemented method, program, and system for identifying non-text element suitable for communication in multi-language environment

ABSTRACT

A computer implemented method, a program, and a system for effectively providing versatile non-text information suitable for use in a multi-language environment. The method includes the steps of: receiving search results of a database using a search criterion in a certain language and a search criterion in another language corresponding to the search criterion in which specific language attributes are associated with non-text elements that are included in the search results; scoring the non-text elements included in the search results depending on a similarity to another element with which a different language attribute is associated; and identifying at least one of the non-text elements included in the search results on the basis of the scores.

CROSS-REFERENCE

This application claims priority under 35 U.S.C. §119 from JapanesePatent Application No. 2012-201909 filed on Sep. 13, 2012 the entirecontents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention generally relates to an information processingtechnique, and more specifically, it relates to a computer implementedmethod, program, and system for identifying a non-text element suitablefor communication in a multilanguage environment.

BACKGROUND

In recent years, a search technique for non-text information, such asimage and sound, has been developed and is becoming widespread. Forexample, users can search for related images present on the Internet byinputting one or more search keywords to execute an image search.

There may be cases where it is intuitive, flexible, and convenient touse non-text information (image, sound, etc.) in communication. However,since the non-text information can be interpreted differently dependingon the person, miscommunication tends to occur. For example, since imageicons have different meanings among communities having differentcultures, there is a possibility that miscommunication may occur amongpeople who belong to different communities.

SUMMARY

One of the objectives of the present invention is to provide a computerimplemented method, program, and system for identifying a non-textelement suitable for communication in a multilanguage environment.

To achieve the above objective, a computer implemented method, aprogram, and a system for identifying a non-text element suitable forcommunication in a multilanguage environment are provided. The methodincludes the step of receiving search results of the database using asearch criterion in a certain language and a search criterion in anotherlanguage corresponding to the first search criterion. Specific languageattributes are associated with non-text elements included in the searchresults. The method further includes the steps of scoring the non-textelements included in the search results based on their similarity toanother element with which a different language attribute is associatedand identifying at least one of the non-text elements included in thesearch results based on the scores.

Preferably, in the scoring step, a non-text element having a highsimilarity to another element with which a different language attributeis associated is given a high score. The method may further include thestep of translating the search criterion in a certain language by usinga translation engine to generate a search criterion in another language.

Preferably, the non-text elements are images and/or sound. The imagessearched for in the present invention include a still image, a movingimage, and any other forms of image.

Preferably, the method further includes the steps of searching thedatabase by using the search criterion in the certain language and thesearch criterion in the other language corresponding to the searchcriterion and associating the language used for the search, as alanguage attribute, with the non-text elements included in the searchresults.

Preferably, the scoring step includes the step of calculating scores forshapes included in the image elements by comparing a plurality ofnon-text elements. The step of giving shapes scores may include:extracting contour information of the image elements and comparing thecontour information extracted from the plurality of image elements,wherein, preferably, contour information that is larger, smaller, and/ordifferent in orientation is compared. The step of comparing the contourinformation may include referring to a shape-pattern classificationdictionary prepared in advance so that a plurality of non-text elementscan be compared in consideration of a related shape-patternclassification.

Preferably, the scoring step includes the step of calculatingcolor-component scores for information about color components includedin the image elements by comparing the plurality of non-text elements.Calculating the color-component scores may include comparing therepresentative color component values of the entire image for theplurality of image elements. Preferably, the representative value of thecolor components is the mean, median, mode and/or a significant value ofthe color component values of the image element in question.

Preferably, the method further includes the step of calculatingsmoothness scores based on the smoothness of the image elements.Calculating the smoothness scores includes dividing each of the imageelements into a plurality of regions in accordance with predeterminedrules, calculating the proportion of the same color for each of theplurality of regions, and calculating the smoothness scores based on theproportion of the same color of each of the plurality of regions. Themethod further includes the step of attempting character recognition ofa character included in the image element by using an optical characterrecognition technique and the step of calculating character recognitionscores based on the result of character recognition.

Preferably, the step of scoring the non-text elements includes the stepof calculating a total score based on at least one of the shape score,the color-component score, the smoothness score, and the characterrecognition score of each of the non-text elements. The method furtherincludes t the step of identifying at least one of the non-text elementsincluded in the search results based on the total score.

Although the outline of the present invention has been described aboveas a computer implemented method for identifying a non-text elementsuitable for communication in a multilanguage environment, the presentinvention can also be a program, a program product, software, a softwareproduct, and a system or unit.

Examples of the program product and the software product can includestorage media that store the above-described program and software andmedia that transmit the program and software. The program can cause acomputer to execute the steps of the above-described method.

The outline of the present invention is not all the necessary featuresof the present invention; it should be noted that the present inventioncan also include a combination or subcombination of the components.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an image search system of anembodiment of the present invention.

FIG. 2 is a flowchart showing the overall operation of the image searchsystem of the embodiment of the present invention.

FIG. 3 is a flowchart showing the first half of the operation of acontour-score calculating process or a color-component-score calculatingprocess across languages in the embodiment of the present invention.

FIG. 4 is a flowchart showing the latter half of the operation of thecontour-extraction or color-component matching process across languagesin the embodiment of the present invention.

FIG. 5 is a flowchart showing still more details of the contour-scorecalculating process across languages in the embodiment of the presentinvention.

FIG. 6 is a flowchart showing still more details of thecolor-component-score calculating process across languages in theembodiment of the present invention.

FIG. 7 is a flowchart showing the details of a smoothness-scorecalculating process across languages in the embodiment of the presentinvention.

FIG. 8 is an image diagram for explaining a non-text-informationdatabase in the embodiment of the present invention.

FIG. 9 is a diagram for explaining an example of search results of theimage search system in the embodiment of the present invention.

FIG. 10 is a diagram for explaining an example of search results of theimage search system in the embodiment of the present invention.

FIG. 11 is a diagram for explaining an example of search results of theimage search system in the embodiment of the present invention.

FIG. 12 is a diagram showing an example of the hardware configuration ofan information processing unit suitable for implementing the imagesearch system of the embodiment of the present invention.

DETAILED DESCRIPTION

Although an embodiment of the present invention will be described indetail below based on the drawings, it is to be understood that theembodiment does not limit the invention described in the scope of claimsand that all of combinations of features described in the embodiment arenot essential for the solutions of the invention.

The present invention can be achieved in many different forms and shouldnot be limited only to the description of the embodiment. The sameelements are given the same reference numerals throughout thedescription of the embodiment.

In an embodiment of the present invention, a user is presented withimage icons that match search criteria that are input by the user andprocessed in an image search system. In particular, in an embodiment ofthe present invention, versatile image icons suitable for use incommunication among people who belong to communities having differentcultures and languages are presented to the user.

In an embodiment of the present invention, a query including a searchcriterion (keyword) based on user input is translated into a pluralityof languages by a translation engine. Then, an image search is executedbased on the original keyword and the translated keyword. In theembodiment of the present invention, images included in the results ofthe image search are each given a specific language attribute.

Next, in the embodiment of the present invention, the images included inthe image search results are subjected to a character recognitionanalysis, color detection, contour detection, and smoothness detection.In the contour detection and the color detection, feature values areextracted for the contours and the color components of the individualimages. Next, the degree of presence of similar images across thelanguages in terms of contour and/or color components is analyzed usingthe extracted feature values. In the case where there is a high degreeof presence of similar images of a certain image, to which otherlanguage attributes are given, the certain image is given a high score.

Furthermore, in the embodiment of the present invention, the characterrecognition analysis and the smoothness detection are performed todetermine whether the individual images are suitable as communicationmeans. Specifically, if a character is included, language dependencyincreases. Therefore, an image that has a high degree of inclusion ofcharacters is given a high score as a result of character recognitionanalysis. Furthermore, to exclude images having too much information andnot suitable as a communication means, such as pictures, images havinglow smoothness are given a high score as a result of smoothnessdetection.

In the embodiment of the present invention, the total scores of theindividual images can be obtained based on the scores obtained by thecharacter recognition analysis, the color detection, the contourdetection, and the smoothness detection, by normalizing the four scoresand calculating the average value thereof. Then, an image icon suitableas communication means that least depends on a language in amultilanguage environment is identified in accordance with the totalscores and is presented to the user.

A conceivable use case of the image search system of the embodiment ofthe present invention is that an image icon suitable for communicationin a multilanguage environment, which is obtained by accessing the imagesearch system via mobile terminals, such as smart phones, is displayedon the mobile terminals and shared by people who cannot understandmutual languages so that communication among them is enabled.

Another conceivable use case is that, when distributing a message ornews to many people, an image icon that indicates the message or news,obtained by the image search system, is distributed. Many target peoplecannot read characters. For such people, image icons presented by theimage search system, suitable for communication in a multilanguageenvironment, can be effective information sources.

Yet another use case is when a literary work designed to be usedlocally, such as an existing brochure, is distributed globally, theimage search system of the embodiment of the present invention can beused to verify whether an image icon included in the literary work canbe understood globally, and if it cannot be understood, to obtain apossible substitute.

The embodiment of the present invention will be described in more detailhereinbelow with reference to FIGS. 1 to 12.

FIG. 1 is a functional block diagram of an image search system 100 ofthe embodiment of the present invention. The components shown in thefunctional block diagram in FIG. 1 can be implemented by loading anoperating system and a computer program, such as an application program,stored in a hard disk drive 13 or the like, in a main memory 4 andcausing a CPU 1 to read them and hardware resources and software tocooperate with each other.

The image search system 100 of the embodiment of the present inventionincludes an input/output section 105, a keyword extraction section 110,an image search section 115, a search-results storage section 120, asearch-results analysis section 125, a total-score calculation section150, a total-score storage section 155, and a presentation-imagedetermination section 160.

The input/output section 105 provides an input and output interfacebetween the image search system 100 and a user or another computersystem. In the embodiment of the present invention, for example, theuser inputs image search criteria via the input/output section 105.

The keyword extraction section 110 of the embodiment of the presentinvention reads the search criteria input via the input/output section105 to extract one or more search keywords and passes the keywords tothe image search section 115. In the embodiment of the presentinvention, the search criteria include the description of communicationand a language attribute. For example, if a natural sentence “anearthquake warning has been issued. Be alert.” and a language attributeJapanese are input as search criteria, the keyword extraction section110 provides, for example, a function of extracting search keywords“earthquake”, “warning”, and “alert”.

The keyword extraction section 110 of the embodiment of the presentinvention implements keyword extraction by using the well-known tf-idfmethod. The term tf is an abbreviation for term frequency, and idf is anabbreviation for inverse document frequency. For example, specifically,for individual words i obtained from a document j by a morphologicalanalysis, if a value obtained by multiplying tf_(i,j) and idf_(i), whichare obtained by the following expressions, is a predetermined thresholdvalue or more, the word i may be extracted as a search keyword.

$\begin{matrix}{{{tf}_{i,j} = \frac{n_{i,j}}{\sum\limits_{k}\; n_{k,j}}}{{idf}_{i} = {\log\frac{D}{\left\{ {d:{d \ni t_{i}}} \right\} }}}} & \left\lbrack {{Expression}\mspace{14mu} 1} \right\rbrack\end{matrix}$where n_(i,j) is the frequency of appearance of the word i in thedocument j, |D| is the number of documents, and |{d:d

t_(i)}| is the number of documents including the word i.

Furthermore, the keyword extraction section 110 of the embodiment of thepresent invention translates the extracted search criteria or searchkeywords to one or more predetermined languages to obtain correspondingsearch keywords and passes the keywords and the language attributesthereof to the image search section 115. The predetermined languages maybe predetermined main languages, may be designated by the user eachtime, or may be a combination thereof. Preferably, the predeterminedlanguages include the language of the other end of the communication.

The image search section 115 of the embodiment of the present inventionmakes an inquiry to a non-text-information database 180 by using a queryincluding the search keywords extracted by the keyword extractionsection 110 and their language attributes to obtain a plurality ofimages that match the keywords. It is preferable to generate queries forthe individual language attributes and to make inquiries to thenon-text-information database 180. Images obtained as search results ofthe image search section 115 are each given one or more languageattributes. The language attributes given to the images of the searchresults may be the language attributes given to the search keywords thatare used when the image search section 115 performed the search.

Referring to FIG. 8, an example of the non-text-information database 180of the present application will be described in more detail. FIG. 8 isan image diagram for explaining the non-text-information database 180 ofthe embodiment of the present invention. The non-text-informationdatabase 180 includes a language list 505, a first character list 510 bylanguage, a word list 515 by language and first character, a pointer 520by language, first character, and word, and a content-file storagesection 525.

Data to be stored in the non-text-information database 180 of thepresent application can be obtained by crawling on a web server (notshown) on the Internet. Specifically, the non-text-information database180 of the embodiment of the present invention obtains information on animage on the Internet, such as the file name of the image, an altattribute, such as a substitute file associated with the image, and textin the vicinity of the image, from a crawling engine (not shown) togenerate meta information (index keywords). At that time, one or morelanguage attributes of the image are obtained by finding a languageattribute assigned to the image or a web page including the image, thefile name, the encoding system, or the like.

The non-text-information database 180 of the present application formsindices for the images to be stored from the meta information and thelanguage attributes. Specifically, language attributes are stored in thelanguage list 505, the first characters of index keywords are stored inthe first character list 510 by language, and index keywords are storedin the word list 515 by language and first character, and pointers forcontent files 530 stored in the content-file storage section 525 arestored in the pointer 520 by language, first character, and word. Theindividual tables 505 to 520 are indicated by the pointers.

The pointer 520 by language, first character, and word includes aranking score that indicates the popularity of the image in a pluralityof images having the same keyword index. Although the ranking score canby obtained by measuring the number of times that the image is clickedin the image search result, a method therefor can be designed by thoseskilled in the art, and thus, it is not described here in detail.

When an inquiry is given to the non-text-information database 180 of theembodiment of the present invention by using a query including a searchkeyword and its language attribute, first, matching between the languageattribute in the query and the language list 505 is performed, in whichmatching between the first character list 510 by language correspondingto the target language attribute and the first character of the searchkeyword included in the query is performed. Next, matching between theword list 515 by language and first character corresponding to thematched first character and the search keyword in the query isperformed, in which the matched pointer 520 by language, firstcharacter, and word is identified. A predetermined number of contentfiles 530 having high ranking scores stored in the content-file storagesection 525 are returned as responses to the query by using theidentified pointer 520 by language, first character, and word.

The content files 530 themselves can be stored in the content-filestorage section 525, or alternatively, uniform resource locators (URLs)that indicate the locations of contents on the Internet may be stored inthe content-file storage section 525 and may be returned to. In thelatter case, the image search section 115 obtains the image in questionfrom the web server on the Internet by using the returned URL.

In the embodiment of the present invention, the language list 505, thefirst character list 510 by language, and the word list 515 by languageand first character can be configured such that frequently used columnsare dynamically moved to higher levels to increase the search speed.

The search-results storage section 120 of the embodiment of the presentinvention stores the search results including the plurality of imagesthat the image search section 115 has obtained. In the search-resultsstorage section 120, the individual images of the search results areassociated with one or more language attributes.

The search-results analysis section 125 of the embodiment of the presentinvention analyzes the search results including the plurality of imagesthat are found by the image search section 115 and stored in thesearch-results storage section 120. The search-results analysis section125 includes a contour detection section 130, a color detection section135, a smoothness detection section 140, and a character-recognitionanalysis section 145.

The contour detection section 130 of the embodiment of the presentinvention extracts feature points of objects included in the individualimages to obtain contour information and analyzes the similarity of thecontour information among the images. As a result, if target imagesoften have high similarity of contour information to another image givena different language attribute, the images are given a higher “contourscore”.

The color detection section 135 of the embodiment of the presentinvention analyzes the similarity of color components among the imagesafter extracting the color components of the individual images. As aresult, an image that often has high similarity between it and an imagehaving color components given a different language attribute is given ahigh “color-component score”. Such scoring is performed to exclude ahighly culture specific image in view of color components whenexpressing a certain event. For example, although the color of mailboxesin America is blue, it is red in Japan, England, etc., green in thePeople's Republic of China, and yellow in Germany, France, etc.Accordingly, when an image icon of a mailbox is searched for, an imagethat cannot be recognized as a mailbox without color has a lowpossibility that it is recognized in a multilanguage environment, andthus, it is necessary to rate such an image low.

The smoothness detection section 140 of the embodiment of the presentinvention obtains smoothness scores for the individual images based onthe proportion of subregions of a uniform color. The smoothnessdetection section 140 gives an image having low smoothness a high“smoothness score”. This is because many images with high smoothness arelandscape pictures or the like, which are not intuitive and clear due totoo large amount of information and thus are often unsuitable forcommunication.

The character-recognition analysis section 145 of the embodiment of thepresent invention analyzes the degree of inclusion of characters thatdepend on languages in the individual images of the search results andgives images with a low degree a high “character recognition score”.This is because an image including many language-dependent characters isuseless as communication means for persons who cannot understand thelanguage. The character-recognition analysis section 145 can beimplemented by applying an existing optical character recognitiontechnique (simply referred to as “OCR”).

Calculations of “contour score”, “color-component score”, “smoothnessscore”, and “character recognition score” will be described later indetail.

The total-score calculation section 150 of the embodiment of the presentinvention receives “element scores”, that is, a “smoothness score”, a“character recognition score”, a “color-component score”, and a “contourscore”, from the search-results analysis section 125, and gives a “totalscore” to each of the images on the basis thereof. The total scorescalculated by the total-score calculation section 150 of the embodimentof the present invention are stored in the total-score storage section155. For example, in the embodiment of the present invention, the “totalscore” is obtained by normalizing the “smoothness score”, the “characterrecognition score”, the “color-component score”, and the “contour score”and thereafter calculating the mean value thereof.

The presentation-image determination section 160 of the embodiment ofthe present invention determines an image to be presented to the user inaccordance with the total scores of the individual images, which arestored in the total-score storage section 155, obtains the determinedimage from the search-results storage section 120, and passes the imageto the input/output section 105 to present to the user. In theembodiment of the present invention, an image having the highest totalscore of the images included in the search results is identified andpresented to the user.

FIG. 2 is a flowchart 200 showing the overall operation of the imagesearch system 100 of the embodiment of the present invention. Theprocess starts at step 205, and in step 210, input of a searchcriterion, such as a keyword or a natural sentence, is received in aspecific language from a user. If a natural sentence is input as asearch criterion, in step 210, the image search system 100 performskeyword extraction using the tf-idf method described in connection withthe keyword extraction section 110.

The process moves to step 215, in which the search keyword in thespecific language, which is extracted in step 210, is translated intoother predetermined languages. Preferably, the other languages include alanguage that a target audience uses in communication in a multilanguageenvironment and include one or more predetermined main languages. Theother languages may be designated by the user. In the embodiment of thepresent invention, a set of keywords in four languages, that is,“English”, “Japanese”, “Chinese”, and “Spanish” is finally obtained.

Next, in step 220, an image search is executed using a search keyword inthe plurality of (four) languages. In the embodiment of the presentinvention, image search is performed by forming a query including asearch keyword and a corresponding language attribute for each of thelanguages, images obtained as results thereof are each given thelanguage attribute included in the query, and they are associated witheach other. The results of the image search are stored in thesearch-results storage section 120.

In the embodiment of the present invention, in steps 225 to 240 afterstep 220, the processes for calculating “contour score”,“color-component score”, “smoothness score”, and “character recognitionscore” are executed in parallel, respectively.

After the processes for calculating “contour score”, “color-componentscore”, “smoothness score”, and “character recognition score” in steps225 to 240 have been completed, the process moves to step 245. In step245, total scores are calculated for the individual images of the searchresults based on the “smoothness score”, “character recognition score”,“contour score”, and “color-component score”. In the embodiment of thepresent invention, the total score is calculate by normalizing the“smoothness score”, “character recognition score”, “contour score”, and“color-component score” and thereafter obtaining the simple mean valuethereof.

Next, the process moves to step 250, in which an image to be presentedis determined on the basis of the total score calculated in step 245,and the determined image is presented to the user. In the embodiment ofthe present invention, an image having the highest total score ispresented to the user. The process moves to step 255, in which theprocess ends.

An example of the contour-score calculating process or thecolor-component-score calculating process across languages in theembodiment of the present invention will be described in more detailwith reference to flowcharts in FIGS. 3 and 4. Note that thecontour-score calculating process and the color-component-scorecalculating process correspond to step 225 and 230 in the flowchart 200in FIG. 2, respectively.

In the flowcharts shown in FIGS. 3 and 4, the contour-score calculatingprocess or the color-component-score calculating process are performedfor an image I_(ij) to which a language i is given as a languageattribute. In the embodiment of the present invention, the steps inFIGS. 3 and 4 are executed by the contour detection section 130 or thecolor detection section 135.

FIG. 3 is a flowchart showing the first half of the operation of thecontour-score calculating process or the color-component-scorecalculating process across languages in the embodiment of the presentinvention. The process starts from step 305, and an attempt to identifya language m other than the target language i is made in step 310.

The process moves to step 315, in which it is determined whether theother unprocessed language m is present. If it is determined in step 315that the other language m is present, the process moves to step 320along the arrow of YES, in which an attempt to extract an image I_(mn)to which the language attribute of the language m is given is made, andnext in step 325, it is determined whether the image I_(mn) issuccessfully extracted.

If it is determined in step 325 that the image I_(mn) is successfullyextracted, the process moves to step 330 along the arrow of YES, inwhich a matching score S_(mn) of the target image I_(ij) and the imageI_(mn) extracted in step 320 is calculated. Since the specific processof the contour matching or the color component matching will bedescribed later in detail using flowcharts shown in FIGS. 5 and 6, it isnot described here.

The process moves to step 335, in which it is determined whether thematching score S_(mn) obtained in step 330 is larger than a maximummatching score T_(m) obtained at previous matching between the targetimage I_(ij) and the image I_(mn) to which the language m is given as alanguage attribute. In the embodiment of the present invention, for thecase where matching for the target image I_(ij) is performed first, themaximum matching score T_(m) is set to 0 as an initial value.

If it is determined in step 335 that the matching score S_(mn) obtainedin step 330 is larger than the maximum matching score T_(m) obtained atprevious matching, the process moves to step 340, in which the maximummatching score T_(m) is replaced with the matching score S_(mn) obtainedin step 330, and thereafter, the process moves to step 320. If it isdetermined in step 335 that the matching score S_(mn) obtained in step330 is not larger than the maximum matching score T_(m) obtained atprevious matching, the process returns to step 320.

After the process returns to step 320, the above process from step 320to step 340 is repeated until the image I_(mn) is no longer present.During the repetition, if it is determined in step 325 that the imageI_(mn) is not present, the process comes out of the loop from the arrowNO and returns to step 310, from which the subsequent processes arerepeated for another language. During the repletion of the process instep 310 and the subsequent processes, if it is determined in step 315that any more unprocessed languages are not present, the process shiftsto the flowchart in FIG. 4 via a mark A in step 345 along the arrow ofNO.

FIG. 4 is a flowchart showing the latter half of the operation of thecontour-extraction or color-component matching process across languagesin the embodiment of the present invention. The process starts from themark A in step 345, and in step 405, the next image I_(ik) in which j≠kholds is extracted from the set of images to which the language i isgiven as a language attribute.

Next, it is determined in step 410 whether the image I_(ik) is present.If it is determined in step 410 that the image I_(ik) is present, theprocess moves to step 415, in which a matching score O_(k) of the targetimage I_(ij) and the image I_(ik) extracted in step 405 is calculated.Since the details of the specific process of the contour matching or thecolor component matching will be described using flowcharts shown inFIGS. 5 and 6, they are not descried here.

The process moves to step 420, in which the value O_(k) calculated instep 415 is added to a cumulative value P of the previous matchingscores of the target image I_(ij) and images to which the same languagei is given as a language attribute. Thereafter, the process returns tostep 405, from which the process is repeated until it is determined instep 410 that the image I_(ik) is not present. If it is determined instep 410 that the image I_(ik) is not present, the process moves to step425.

In step 425, the color-component score or the contour score of the imageI_(ij) is calculated by weighing T_(m) and P. The process moves to step430, at which it ends.

The process of calculating “contour score” in the embodiment of thepresent invention will be described in more detail using FIG. 5. FIG. 5is a flowchart showing still more details of the contour-scorecalculating process across languages in the embodiment of the presentinvention. Note that the process of this flowchart corresponds to step330 in the flowchart of FIG. 3 or step 415 in the flowchart of FIG. 4.The steps shown in FIG. 5 are executed by the contour detection section130.

The process starts from step 505, and in step 510, feature points of twoimages (here, images A and B) are extracted. Here, the images A and Bcorrespond to the images I_(ij) and I_(mn) in step 330 and correspond tothe images I_(ij) and I_(ik) in step 415. The feature points of theimages can be calculated in accordance with the known Gabor filtermethod, and specifically, can be obtained by the following expression.

${g\left( {x,{y;\lambda},\theta,\psi,\sigma,\gamma} \right)} = {{\exp\left( {- \frac{x^{\prime 2} + {\gamma^{2}y^{\prime 2}}}{2\sigma^{2}}} \right)}{\cos\left( {{2\pi\frac{x^{\prime}}{\lambda}} + \psi} \right)}}$where

-   -   x′=x cos θ+y sin θ    -   y′=−x sin θ+y cos θ        where λ is the cosine component of the wavelength, θ is the        orientation of the striped pattern of a Gabor function, ψ is the        phase offset, γ is the spatial aspect ratio, and σ is the size        of the Gaussian envelope.

The process moves to step 515, in which the minimum distance dist1between the feature points of the image A and the feature points of theimage B extracted in step 510 is calculated. The minimum distance hereis the sum total of the distances between the feature points of theimage A and the feature points of the image B in the case wherecombinations of a feature point of the image A and a feature point ofthe image B are formed without exception so that the sum total becomesthe minimum. Next, in step 525, the image A is laterally reversed, andthe minimum distance distR between the feature points of the reversedimage A and the feature points of the image B is calculated.

Next, the process moves to step 530, in which an optimum enlargement orreduction ratio for the images A or B is calculated. The optimumenlargement or reduction ratio is a ratio for enlarging or reducing oneof the images A and B so that the images A and B have the same size (thesame width and height if they have different aspect ratios). In step535, the minimum distance distX between the feature points of the imageA and the feature points of the image B enlarged or reduced using theenlargement ratio or the reduction ratio calculated in step 530 iscalculated.

Next, in step 540, the image A is laterally reversed, and an optimumenlargement or reduction ratio for the laterally reversed image A andthe image B is calculated. Next, in step 545, the minimum distancedistXR between the feature points of the images A and B, which areenlarged or reduced using the enlargement or reduction ratio calculatedin step 540, is calculated.

The process moves to step 550, in which the minimum value of the valuesdist1, distR, distX, and distXR is selected as distOpt. Note that thevalues distR, distX, and distXR are values in which, if a certain imageis an image obtained merely by laterally reversing and/or enlarging orreducing the other image, they should be treated as similar images.Next, in step 555, the value distOpt is stored as a contour matchingscore, and the process ends at step 560.

The process of calculating “color-component score” in the embodiment ofthe present invention will be described in more detail using FIG. 6.FIG. 6 is a flowchart showing still more details of thecolor-component-score calculating process across languages in theembodiment of the present invention. Note that the process of thisflowchart corresponds to step 330 in the flowchart of FIG. 3 or step 415in the flowchart of FIG. 4. The steps shown in FIG. 6 are executed bythe color detection section 135.

The process starts from step 605, and in step 610, predetermined colorcomponents, here, RGB components, are extracted for two images (images Aand B). Here, the images A and B correspond to the images I_(ij) andI_(mn) in step 330 and correspond to the images I_(ij) and I_(ik) instep 415.

Next, in step 615, the mean values of the individual color components,that is, red (R), green (G), and blue (B), of the images A and B arecalculated, and in step 620, the absolute values of the differencesbetween the mean values of the individual color components calculated instep 615 are calculated. Next, in step 625, the sum (colorVal) of thedifferences between the mean values of the individual color componentsis calculated. Then, in step 630, the value colorVal is stored as amatching score concerning color components, and the process ends in step635.

Next, the process of calculating the “smoothness score” in theembodiment of the present invention will be described in detail usingthe flowchart shown in FIG. 7. Note that the process of the flowchartshown in FIG. 7 corresponds to step 235 in the flowchart 200 of FIG. 2.The steps in this flowchart are executed by the smoothness detectionsection 140.

First, the process starts from step 705 after execution of step 220, andin step 710, one image is extracted from images included in the resultsof image search in step 220. Next, in step 715, the image extracted instep 710 is divided into subregions (here, 3-by 3-pixel regions) inaccordance with predetermined rules. The process moves to step 720, inwhich one of the regions divided in step 715 is extracted, and in step725, the color components (here, RGB components) of the region extractedin step 720 are extracted.

The process further advances to step 730, in which it is determinedwhether the number of points of the same color present in the targetregion is larger than a predetermined percentage (here, 50%) on thebasis of the color components extracted in step 725. If it is notdetermined in step 730 that the number is larger than the predeterminedproportion, the process moves to step 740 along the arrow of NO. If itis determined in step 730 that the number is larger than thepredetermined proportion, the process moves to step 735 along the arrowof YES. In step 735, “the number of smooth regions”, which is avariable, is increased by one, and the process moves to step 740.

In step 740, it is determined whether the unprocessed region obtained bydividing in step 715 remains. If it is determined in step 740 that itremains, the process returns to step 720 along the arrow of YES, and thesubsequent process is repeated. If it is determined in step 740 that noregion remains, the process moves to step 745 along the arrow of NO, inwhich the smoothness score of the target image is calculated accordingto the following expression.Smoothness score=number of smooth regions/total number of dividedregions

After the smoothness score is calculated in step 745, the process movesto step 750, in which it is determined whether the unprocessed imageremains in the images obtained by the image search in step 220. If it isdetermined in step 750 that there is a remaining unprocessed image, theprocess returns to step 710 along the arrow of YES, and the subsequentprocess is repeated. If it is determined in step 750 that no unprocessedimage remains, the process moves to step 755 along the arrow of NO, inwhich the process ends (moves to step 245).

Next, the process of calculating “character recognition score” in theembodiment of the present invention will be described. As has alreadybeen described, the process of calculating “character recognition score”is executed by the character-recognition analysis section 145. Using theexisting optical character recognition technique allows the probabilityof presence of a character in a region in which a character may beincluded in the image (referred to as a “character region”) to beobtained by identify the character region and performing matchingbetween an image present in the character region and character font dataprepared in advance. In the embodiment of the present invention, thereciprocal of the probability of the presence of a character in thecharacter region is calculated as “character recognition score”.

As described above, the total-score calculation section 150 of theembodiment of the present invention obtains the total score bynormalizing the “contour score”, “color-component score”, “smoothnessscore”, and “character recognition score” and then calculating the meanvalue thereof. The individual scores can be normalized so they fallwithin predetermined ranges in accordance with predetermined rules. Forexample, the normalized contour score of the image i can be obtained bythe following expression:Normalized contour score of image i=(maximum value of contour scores ofall images+M1)/(contour score of image i+M1)where M1 is a constant other than 0 to prevent the above expression fromdivision by zero.

For example, the normalized color-component score of the image i can beobtained by the following expression:Normalized color-component score of image i=(maximum value ofcolor-component scores of all images+M2)/(color-component score of imagei+M2)where M2 is a constant other than 0 to prevent the above expression fromdivision by zero.

Referring to FIGS. 9 to 11, examples of search results of the imagesearch system in the embodiment of the present invention will bedescribed. Note that, although FIGS. 9 to 11 are expressed as monochromescreens because of the constraint that color drawings cannot be used inapplication drawings, images of actual examples are color icons. FIG. 9is a diagram illustrating an example of search results of the imagesearch system in the embodiment of the present invention. The exampleshown in FIG. 9 shows a case where an English word, “earthquake”, isselected as an input keyword. Note that, in this case, correspondingtranslated keywords are Japanese “jishin”, Chinese “dizhen”, and Spanish“terremoto”. In this example, since an image obtained as a fourthcandidate when the search is performed using English “earthquake” hasthe highest total score (total score=0.88), this image is presented tothe user.

FIG. 10 is a diagram for explaining another example of search results ofthe image search system in the embodiment of the present invention. Theexample shown in FIG. 10 shows a case where the English word “tsunami”is selected as an input keyword. Note that, in this case, correspondingtranslated keywords are Japanese “tsunami”, Chinese “haixiao”, andSpanish “tsunami”. In this example, since an image obtained as a fourthcandidate when the search is performed using English “tsunami” has thehighest total score (total score=0.96), this image is presented to theuser.

FIG. 11 is a diagram for explaining still another example of searchresults of the image search system in the embodiment of the presentinvention. The example shown in FIG. 11 shows that an English word“exit” is selected as an input keyword. Note that, in this case,corresponding translated keywords are Japanese “deguchi”, Chinese“chukou”, and Spanish “salida”. In this case, since an image obtained asa fifth candidate when the search is performed using Japanese “deguchi”has the highest total score (total score=0.96), this image is presentedto the user.

FIG. 12 is a diagram showing an example of the hardware configuration ofan information processing unit suitable for implementing the imagesearch system of the embodiment of the present invention. Theinformation processing unit includes a central processing unit (CPU) 1and a main memory 4 connected to a bus 2. Removable storages (externalstorage systems in which storage media can be replaced), such as harddisk drives 13 and 30, CD-ROM drives 26 and 29, a flexible disk drive20, an MO drive 28, and a DVD drive 31, are connected to the bus 2 via afloppy disk controller 19, an IDE controller 25, and an SCSI controller27.

Storage media, such as a flexible disk, an MO, a CD-ROM, and a DVD-ROM,are inserted into the removable storages. Such storage media, the harddisk drives 13 and 30, and a ROM 14 can store computer program codes forgiving instructions to the CPU and so on in cooperation with anoperating system to implement the present invention. The computerprograms are loaded in the main memory 4 so that they are executed. Thecomputer programs can also be compressed or divided into a plurality ofpieces and stored in a plurality of media.

The information processing unit receives input from input devices, suchas a keyboard 6 and a mouse 7, via a keyboard/mouse controller 5. Theinformation processing unit is connected to a display 11 for presentingvisual data to users via a DAC/LCDC 10.

The information processing unit can be connected to a network via anetwork adapter 18 (Ethernet® or the like) to communicate with anothercomputer and so on. Although not shown, the information processing unitcan also be connected to a printer via a parallel port or to a modem viaa serial port.

It will be easily understood with the above description that theinformation processing unit suitable for implementing the image searchsystem according to the embodiment of the present invention can beimplemented by an information processing unit, such as a common personalcomputer, a workstation, and a main frame, or a combination thereof.However, these components are merely examples, and all the componentsare not absolutely necessary components of the present invention.

It will also be obvious to those skilled in the art that various changescan be made in the hardware components of the information processingunit used in the embodiment of the present invention, such as combininga plurality of machines and distributing the functions thereto. Thesechanges are of course included in the spirit of the present invention.

The image search system of the embodiment of the present inventionadopts an operating system that supports a graphical user interface(GUI) multiwindow environment, such as the Windows® operating systempresented by Microsoft Cooperation, Mac OS® presented by Apple Inc., andthe UNIX® system (for example, AIX® presented by International BusinessMachines Cooperation).

Thus, it can be understood that the image search system used in theembodiment of the present invention is not limited to use in a specificoperating system environment.

As can be understood by those skilled in the art, the present inventioncan be embodied as a system, a method, or a computer program product.Therefore, the present invention can adopt an embodiment as wholehardware, whole software (including firmware, resident software, andmicrocode) or an embodiment in which software, which is generallyreferred to as “circuit”, “module”, or “system”, and hardware arecombined. Furthermore, the present invention can also adopt the form ofa computer program product embodied as a physical expression mediumhaving computer-usable program code implemented by the medium.

The present invention can also use a combination ofcomputer-usable/computer-readable media. Examples of thecomputer-usable/computer-readable media include electronic, magnetic,optical, electromagnetic, infrared, and semiconductor systems, units,devices, and propagation media, although not limited thereto. Aninexhaustive list of more specific examples of the computer-readablemedia includes electrical connection having a lead, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or a flashmemory), an optical fiber, a portable compact disk read-only memory(CD-ROM), an optical storage device, a transmission medium that supportsthe Internet or an intranet, and a magnetic storage device.

It should be noted that since programs are electronically obtained byoptically scanning, for example, paper or another medium, and arecompiled, interpreted, processed by an appropriate method as necessary,and stored in a computer memory, the computer-usable/computer-readablemedia may be paper on which the program is printed or other appropriatemedia. The computer-usable/computer-readable media may be any mediacapable of including, storing, communicating, propagating, or carrying aprogram used by an instruction execution system, unit, or device or inrelation thereto. The computer-usable media can include a propagateddata signal including a program code that a computer implementedtogether therewith can use in a base band or as part of carrier waves.The computer-usable program code can be transmitted using an appropriatemedium including radio, a wire line, an optical fiber cable, and RF, butnot limited thereto.

A computer program code for implementing the present invention can bedescribed using one or a combination of a plurality of program languagesincluding object-oriented programming languages, such as Java,Smalltalk, and C++, and conventional procedural programming languages,such as a C programming language and a similar programming language. Theprogram code can be wholly or partly executed on a user's computer, oras a standalone software package, partly on a user's computer and partlyon a remote computer, or wholly on a remote computer or a server. Withthe latter scenario, the remote computer can be connected to the user'scomputer via any kind of network including a local area network (LAN) ora wide area network (WAN), or to an external computer (for example, viathe Internet using an Internet service provider).

The embodiment of the present invention has been described above withreference to the flowcharts of the method and/or block diagrams of thesystem and computer program product. It will be understood that theflowcharts and/or blocks in the block diagrams and combinations of theflowcharts/or blocks in the block diagrams can be executed according toinstructions of the computer program. It is also possible that computerprogram instructions executed via a processor of a general-purposecomputer, a special-purpose computer, or another programmable dataprocessing unit are given to the processor of the computer or the otherprogrammable data processing unit so as to manufacture a machine thatgenerates means for executing a function/operation designated byflowcharts and/or one or a plurality of blocks.

These computer program instructions can also be stored in acomputer-readable medium that can operate a computer or anotherprogrammable data processing unit in a specific form so as tomanufacture a product including means for executing a function/operationdesignated by flowcharts and/or one or a plurality of blocks.

These computer program instructions can also be loaded in a computer oranother programmable data processing unit to produce a process to beexecuted by the computer so as to provide a process for executing afunction/operation designated by flowcharts and/or one or a plurality ofblocks and so as to execute a series of operation steps on the computeror the other programmable data processing unit.

The flowcharts and the block diagrams in the drawings show thearchitecture, functionality, and executing operations of the system,method, and computer program products according to various embodimentsof the present invention. In this respect, the flowcharts or theindividual blocks of the block diagrams can show modules, segments, orcodes including one or a plurality of executable instructions forimplementing designated logical functions. It should also be noted that,in an alternative implementation example, functions indicated in theblocks are sometimes executed in a different sequence from those shownin the drawings. For example, two blocks shown in sequence are sometimesactually executed substantially at the same time or in reverse order.The blocks in the block diagrams and/or the flowcharts and combinationsof the blocks in the block diagrams and/or the flowcharts can beexecuted by a system mainly composed of hardware for special purpose forperforming a special function or action or a combination of specialpurpose hardware.

It is obvious to those skilled in the art that the above embodiment canbe variously changed or modified. For example, although the embodimentof the present invention has been described on condition that a singleimage is presented to the user, a plurality of images having high totalscores can be presented on the basis of total scores. Furthermore, inthe embodiment of the present invention, although the total score isobtained by normalizing “contour score”, “color-component score”,“smoothness score”, and “character recognition score” and thencalculating the mean value thereof, those skilled in the art can obtainthe total score from “contour score”, “color-component score”,“smoothness score”, and “character recognition score” by variousmethods.

Furthermore, in calculation of “contour score”, it is possible that ashape-pattern classification dictionary is prepared in advance, arelated shape pattern classification is identified with reference to thedictionary, and thereafter a similarity among a plurality of non-textelements is calculated in consideration of the identified patternclassification. Furthermore, in calculation of “color-component score”,the mean value of the color component values of the image is obtained;instead, it is possible to use not only the median, mode, and/or asignificant value of color components of the image but alsorepresentative values of all color components as appropriate.Furthermore, in calculation of “character recognition score”, thecharacter recognition score can be set low when a larger number ofcharacters are included in the image element.

Although the present invention has been described using a still image asan example of non-text information, moving images, sound, etc. can becompiled into a database by indexing so as to be searched for, and anynon-text information whose mutual similarity can be calculated and acombination thereof can also be searched for. For example, for sound, anexisting sound recognition technique can be used instead of the opticalcharacter recognition technique. Of course, the above changes andmodifications can also be included in the technical scope of the presentinvention.

It should be understood that versatile non-text information suitable foruse in a multilanguage environment can be effectively obtained by theembodiment of the present invention.

What is claimed is:
 1. A computer implemented method for identifying anon-text element suitable for communication in a multilanguageenvironment by using a database in which a non-text element can besearched for, the method comprising the steps of: receiving searchresults of the database using a first search criterion in a certainlanguage and a second search criterion in another language correspondingto the first search criterion, wherein specific language attributes areassociated with non-text elements that are included in the searchresults; scoring the non-text elements included in the search resultsdepending on a similarity to another element with which a differentlanguage attribute is associated, wherein at least one non-text elementof the non-text elements is an image, and wherein scoring the at leastone non-text element further comprises: attempting character recognitionof a character included in the image by using an optical characterrecognition technique; calculating character recognition scores for eachcharacter included in the image based on the result of the attemptedcharacter recognition, wherein the character recognition score is low ifthe image includes multiple characters; and identifying at least one ofthe non-text elements included in the search results on the basis of thescores.
 2. The method according to claim 1, wherein, in the scoringstep, a non-text element having a high similarity to another elementwith which a different language attribute is associated is given a highscore.
 3. The method according to claim 1, further comprising the stepof translating the first search criterion in the certain language byusing a translation engine to generate the second search criterion inthe other language.
 4. The method according to claim 1, furthercomprising the steps of: searching the database by using the firstsearch criterion in the certain language and the second search criterionin the other language corresponding to the first search criterion; andassociating the language used for the search, as a language attribute,with the non-text elements included in the search results.
 5. The methodaccording to claim 1, wherein the scoring step includes the step ofcalculating shape scores for information about shapes included in theimage elements by comparing the plurality of non-text elements.
 6. Themethod according to claim 5, wherein the step of giving shape scoresincludes the step of extracting contour information of the imageelements and the step of comparing the contour information extractedfrom the plurality of image elements.
 7. The method according to claim6, wherein, in the step of comparing the contour information, contourinformation that is larger, smaller, and/or different in orientation iscompared.
 8. The method according to claim 6, wherein the step ofcomparing the contour information includes the step of referring to ashape-pattern classification dictionary prepared in advance, in which aplurality of non-text elements are compared in consideration of arelated shape-pattern classification.
 9. The method according to claim1, wherein the scoring step includes the step of calculatingcolor-component scores for information about color components includedin the image elements by comparing the plurality of non-text elements.10. The method according to claim 9, wherein the step of calculating thecolor-component scores includes the step of comparing the representativecolor component values of the entire image for the plurality of imageelements.
 11. The method according to claim 10, wherein therepresentative value of the color components is the mean, median, modeand/or a significant value of the color component values of the imageelement in question.
 12. The method according to claim 1, furthercomprising the step of calculating smoothness scores on the basis of thesmoothness of the image elements.
 13. The method according to claim 12,wherein the step of calculating the smoothness scores includes the stepof dividing each of the image elements into a plurality of regions inaccordance with predetermined rules, the step of calculating theproportion of the same color for each of the plurality of regions, andthe step of calculating the smoothness scores on the basis of theproportion of the same color of each of the plurality of regions. 14.The method according to claim 1, wherein: the step of scoring thenon-text elements includes the step of calculating a total score on thebasis of at least one of a shape score, a color-component score, asmoothness score, and a character recognition score of each of thenon-text elements; and the identifying step includes the step ofidentifying at least one of non-text elements included in the searchresults on the basis of the total score.
 15. A computer system foridentifying a non-text element suitable for communication in amulti-language environment by using a database in which a non-textelement can be searched, the system including a memory, a processorcommunicatively coupled to the memory, and a module configured to carryout the steps of: receiving search results of the database using a firstsearch criterion in a certain language and a second search criterion inanother language corresponding to the first search criterion, whereinspecific language attributes are associated with non-text elements thatare included in the search results; scoring the non-text elementsincluded in the search results depending on a similarity to anotherelement with which a different language attribute is associated, whereinat least one non-text element of the non-text elements is an image, andwherein scoring the at least one non-text element further comprises:attempting character recognition of a character included in the image byusing an optical character recognition technique; calculating characterrecognition scores for each character included in the image based on theresult of the attempted character recognition, wherein the characterrecognition score is low if the image includes multiple characters; andidentifying at least one of the non-text elements included in the searchresults on the basis of the scores.
 16. A non-transitory computerreadable article of manufacture tangibly embodying computer readableinstructions which, when executed, cause a computer to carry out thesteps of a method comprising: receiving search results of the databaseusing a first search criterion in a certain language and a second searchcriterion in another language corresponding to the first searchcriterion, wherein specific language attributes are associated withnon-text elements that are included in the search results; scoring thenon-text elements included in the search results depending on asimilarity to another element with which a different language attributeis associated, wherein at least one non-text element of the non-textelements is an image, and wherein scoring the at least one non-textelement further comprises: attempting character recognition of acharacter included in the image by using an optical characterrecognition technique; calculating character recognition scores for eachcharacter included in the image based on the result of the attemptedcharacter recognition, wherein the character recognition score is low ifthe image includes multiple characters; and identifying at least one ofthe non-text elements included in the search results on the basis of thescores.